Semantic and structural similarities between XML Schemas for integration of ubiquitous healthcare data

Currently, a lot of recent electronic health records are based on XML documents. In order to integrate these heterogeneous XML medical documents efficiently, studies on finding structure and semantic similarity between XML Schemas have been exploited. The main problem is how to harvest the most appropriate relatedness to combine two schemas as a global XML Schema for reusing and referring purposes. In this paper, we propose the novel resemblance measure that concurrently considers both structural and semantic information of two specific healthcare XML Schemas. Specifically, we introduce new metrics to compute the datatype and cardinality constraint similarities, which improve the quality of the semantic assessment. On the basis of the similarity between each element pair, we put forward an algorithm to calculate the similarity between XML Schema trees. Experimental results lead to the conclusion that our methodology provides better similarity values than the others with regard to the accuracy of semantic and structure similarities.